Deep Multimodal Learning with Missing Modality: A Survey
This survey reviews methods for handling missing modalities in deep multimodal learning, enhancing model robustness.
Key Findings
Methodology
The paper reviews deep learning methods for handling missing modalities in multimodal learning, proposing a detailed taxonomy covering modality imputation, representation generation, and architecture adjustments.
Key Results
- Studies show using Generative Adversarial Networks (GANs) to generate missing modality data improves downstream task performance, notably in medical imaging.
- In remote sensing, graph learning methods dynamically fuse modalities, enhancing data integrity and model accuracy.
- Experiments reveal attention mechanism-based architecture adjustments excel in multimodal fusion.
Significance
This study provides a comprehensive review of the multimodal learning field, offering new perspectives and methods for academia and industry, addressing the long-standing issue of modality incompleteness.
Technical Contribution
The paper proposes a new taxonomy, analyzing the technical contributions of existing methods and suggesting new engineering possibilities, such as using generative models for modality generation.
Novelty
This is the first comprehensive survey addressing the missing modality problem in multimodal learning, proposing a new taxonomy and methodological analysis.
Limitations
- Current methods struggle with high missing rate datasets, where generated modality quality needs improvement.
- Generative models' computational demands are high, limiting their use in resource-constrained environments.
Future Work
Future research could focus on improving the quality of generated modalities and optimizing computational efficiency for resource-limited environments.
AI Executive Summary
In multimodal learning, missing modalities pose a common and challenging problem, affecting model performance. Existing methods often assume all modalities are available, but in reality, due to sensor failures, privacy constraints, etc., modalities are often incomplete.
This paper proposes a new taxonomy covering modality imputation, representation generation, and architecture adjustments, aiming to enhance model robustness in the presence of missing modalities. Notably, using Generative Adversarial Networks (GANs) and graph learning methods shows excellent performance in medical imaging and remote sensing.
Despite significant progress, current methods still need improvement on high missing rate datasets. Future research could focus on enhancing the quality of generated modalities and optimizing computational efficiency for broader application deployment.
Deep Analysis
Background
Multimodal learning has become a research hotspot in AI, aiming to combine multiple data modalities to enhance model understanding. However, data modalities are often incomplete in reality, leading to decreased model performance.
Core Problem
The core problem is how to effectively utilize available data for learning when modalities are missing. This issue is particularly prominent in cases of sensor failures and privacy constraints.
Innovation
The paper innovatively proposes a taxonomy covering modality imputation, representation generation, and architecture adjustments, aiming to enhance model robustness.
Methodology
- �� Modality Imputation: Use GANs to generate missing modality data.
- �� Representation Generation: Dynamically fuse modalities using graph learning methods.
- �� Architecture Adjustments: Use attention mechanisms to adapt to different modality combinations.
Experiments
Experiments are conducted on multiple datasets, including medical imaging and remote sensing data, using GANs and graph learning methods for modality generation and fusion.
Results
Results show that using GANs to generate missing modality data significantly improves downstream task performance, especially in medical imaging.
Applications
This method can be applied in medical imaging analysis, remote sensing data processing, enhancing data integrity and model accuracy.
Limitations & Outlook
Current methods still need improvement on high missing rate datasets, and generative models have high computational demands.
Plain Language Accessible to non-experts
Imagine a kitchen where you have various ingredients to make a dish, but sometimes some ingredients are missing. Multimodal learning is like a chef trying to make a delicious dish even when some ingredients are missing. By using substitutes or innovative cooking techniques, the chef can compensate for the missing ingredients, ensuring the dish's flavor remains unaffected.
ELI14 Explained like you're 14
Imagine you're playing a game that requires multiple skills, but sometimes you lose one skill. Multimodal learning is like a game character trying to win even when a skill is lost. By using other skills or finding new strategies, the character can make up for the lost skill, ensuring victory in the game.
Glossary
Multimodal Learning
Combining multiple data modalities to enhance model understanding.
In multimodal learning, models need to handle various types of data, like images and text.
Generative Adversarial Network
A generative model using a generator and discriminator to create realistic data.
Used to generate missing modality data, enhancing model robustness.
Graph Learning
Using graph structures to represent and process relationships between data.
Used in multimodal learning to dynamically fuse different modalities.
Attention Mechanism
A model mechanism that dynamically adjusts focus on input data.
Used in multimodal learning to adapt to different modality combinations.
Representation Generation
Generating representations of missing modalities to enhance model robustness.
Used in multimodal learning to fill in missing modality data.
Open Questions Unanswered questions from this research
- 1 How to improve the quality of generated modalities on high missing rate datasets?
- 2 How to optimize the computational efficiency of generative models for resource-constrained environments?
Applications
Immediate Applications
Medical Imaging Analysis
By generating missing modality data, improve the accuracy of medical imaging analysis.
Long-term Vision
Autonomous Driving
Ensure the safety and reliability of autonomous driving systems in case of sensor failures.
Abstract
During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions.